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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Uncertainty in Measurement: Accuracy and Precision03:37

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Uncertainty in Measurement: Reading Instruments02:46

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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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Using UncertainSCI to Quantify Uncertainty in Cardiac Simulations.

Lindsay C Rupp1,2,3, Zexin Liu1,4, Jake A Bergquist1,2,3

  • 1Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA.

Computing in Cardiology
|February 27, 2023
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Summary
This summary is machine-generated.

UncertainSCI quantifies parameter uncertainty in cardiac simulations using polynomial chaos expansion. This efficient framework improves prediction accuracy and establishes realistic expectations for clinical applications.

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Area of Science:

  • Computational biology
  • Biomedical engineering
  • Numerical analysis

Background:

  • Cardiac simulations are vital for understanding heart function but often lack accuracy due to parameter uncertainty.
  • Existing uncertainty quantification (UQ) methods can be complex to implement in practical simulations.

Purpose of the Study:

  • To develop an efficient and user-friendly UQ framework, UncertainSCI, for cardiac simulations.
  • To address the challenge of parameter uncertainty impacting the reliability of simulation predictions.

Main Methods:

  • Developed UncertainSCI, a UQ framework utilizing polynomial chaos (PC) expansion for stochastic error modeling.
  • Employed non-intrusive methods for efficient parameter space exploration.
  • Created a Python API to simplify the UQ process for users.

Main Results:

  • Demonstrated UncertainSCI's efficiency, stability, and accuracy in creating PC emulators.
  • Quantified sensitivity of torso potentials to heart position uncertainty (boundary element method).
  • Assessed sensitivity of torso potentials to tissue conductivity uncertainty (finite element method).

Conclusions:

  • UncertainSCI enables robust evaluation of simulation sensitivity to parameter uncertainty.
  • The framework helps set realistic expectations for model accuracy and clinical guidance.
  • Facilitates improved reliability and interpretability of cardiac simulation results.